Imports

# Data Analysis
import os
from tqdm import tqdm
import zipfile
import numpy as np
from numpy import asarray
from numpy import save
from numpy import load

# Scikit Learn
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle

# Visualization
from matplotlib import pyplot as plt
#%matplotlib inline
import seaborn as sns

# Tensorflow 2.0
import tensorflow as tf
print(f"Tensorflow Version: {tf.__version__}")
%load_ext tensorboard
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, UpSampling2D, Flatten, Dense, Dropout 
from tensorflow.keras.layers import Conv2DTranspose, Reshape, concatenate, RepeatVector
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.applications.vgg19 import preprocess_input
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TensorBoard, CSVLogger
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
print(f"Imported Tensorflow")

# Scikit Learn image processing
from skimage.color import rgb2lab, lab2rgb, rgb2gray, gray2rgb
from skimage.transform import resize
from skimage.io import imsave
print(f"Imported Scikit Learn Image Processing tools")

Tensorflow Version: 2.3.1
Imported Tensorflow
Imported Scikit Learn Image Processing tools

from utils import *  

Tensorflow Version: 2.3.1

Utilities

def load_pretrained_vgg19_embedding(pretrained_weights):
    
    # Construct VGG19 model without the classifer and weights trained on imagenet data
    # '''Takes (224, 224, 3) RGB and returns the embeddings(predicions) generated on the RGB image'''
    feature_extractor = VGG19(input_shape=(224, 224, 3),
                              include_top = False)
    
    x = feature_extractor.output
    flat = Flatten()(x)
    fc_1 = Dense(1024, activation='relu')(flat)
    do_1 = Dropout(0.2)(fc_1)
    fc_2 = Dense(512, activation='relu')(do_1)
    do_2 = Dropout(0.3)(fc_2)
    output = Dense(9, activation= 'softmax')(do_2)

    embed_model = Model(feature_extractor.inputs, output)
    # Compile model
    embed_model.compile(loss="categorical_crossentropy",
                        optimizer=tf.keras.optimizers.Adam(lr=0.0004),
                        metrics=["accuracy"])
    
    print("Model Compiled")
    
    embed_model.load_weights(pretrained_weights)
    print("Loaded Finetuned Weights")
    
    return embed_model


def block(x, n_convs, filters, strides, activation, block_name):
    '''
    This functions defines a convolution layer(s) in the encoder network.
    Couples a BatchNormalization layer after a convolution layer.
    '''
    for i in range(n_convs):
        x = tf.keras.layers.Conv2D(filters=filters, kernel_size=(3,3), 
                                   strides = strides,activation=activation, 
                                   padding='same', name=f"{block_name}_conv{i+1}")(x)
        
        x = tf.keras.layers.BatchNormalization(name=f"{block_name}_batch_normalization{i+1}")(x)
    return x


def Colorization_Model():
    """
    Builds the entire Neural Coloring model architecture
    """
    embed_input = Input(shape=(9,), name="pretrained_model_output") # Prediction from the classifier
    visible = Input(shape=(224,224,1), name="input_image")          # Input image - L channel from 
                                                                    # Lab color space
    
    # Encoder
    ## Low Level Features Network
    x = block(visible, 1, 64, (2,2), 'relu', 'LL1')
    x = block(x, 1, 128, (1,1), 'relu', 'LL2')
    x = block(x, 1, 128, (2,2), 'relu', 'LL3')
    x = block(x, 1, 256, (1,1), 'relu', 'LL4')
    x = block(x, 1, 256, (2,2), 'relu', 'LL5')
    x = block(x, 1, 512, (1,1), 'relu', 'LL6')
    
    ## Mid-Level Features Network
    # The shape of mid-level network output is a (256, 28, 28) or 784 pixels for each filter
    # This layer shape determines the number of copies of embedding vectors to generate
    x = block(x, 1, 512, (1,1), 'relu', 'ML1')
    x = block(x, 1, 256, (1,1), 'relu', 'ML2')
    
    ## Bridge Network
    # bridge connecting the mid level features with the VGG19 classification output
    # Helps the encoder-decoder model to learn features of each class for better colorization  
    fusion_output = RepeatVector(28*28)(embed_input) # generates 784 copies of the classifier embedding (9,)
    fusion_output = Reshape(([28, 28, 9]))(fusion_output) # Reshape 784 to (28,28,9)
    fusion_output = tf.keras.layers.concatenate([x, fusion_output], axis=-1) # Attach this (28,28,9) to (28,28,256)
    fusion_output = block(fusion_output, 1, 256, (1,1), 'relu', 'FuL')
    
    ## Decoder Network
    decoder_output = block(fusion_output, 1, 128, (1,1), 'relu', 'DL1')
    decoder_output = UpSampling2D((2, 2))(decoder_output)
    decoder_output = block(decoder_output, 1, 64, (1,1), 'relu', 'DL2')
    decoder_output = UpSampling2D((2, 2))(decoder_output)
    decoder_output = block(decoder_output, 1, 32, (1,1), 'relu', 'DL3')
    decoder_output = block(decoder_output, 1, 16, (1,1), 'relu', 'DL4')
    decoder_output = block(decoder_output, 1,  2, (1,1), 'tanh', 'DL5')
    decoder_output = UpSampling2D((2, 2), name='colorization')(decoder_output)
    
    # Define inputs, outputs and compile the model 
    model = Model(inputs = [visible, embed_input], outputs = [decoder_output])
    optimizer = tf.keras.optimizers.RMSprop(lr=0.001)
    model.compile(optimizer=optimizer, 
                loss = 'mse',
                metrics = tf.keras.metrics.RootMeanSquaredError())
    
    return model

def getImages(filelist, transform_size=(224, 224, 3)):
    """Reads image filelist from DATASET and returns float represtation of RGB [0.0, 1.0]"""
    img_list = []
    for filename in tqdm(filelist):
        # 1. Loads JPEG image and converts it to numpy float array.
        image_in = img_to_array(load_img(filename))
        
        # 2. Normalizes the pixel values [0.0, 255.0] => [0.0, 1.0]
        image_in = image_in/255
        # 3. Resizes the image to the desired size
        if transform_size is not None:
            image_in = resize(image_in, transform_size, mode='reflect')
        # 4. Appends the image to a image list
        img_list.append(image_in)
    # 5. convert the images as a numpy array and return the numpy array   
    img_list = np.array(img_list)
    
    return img_list

def preprocess_images(rgb, embed_model, input_size=(224,224,3), embed_size=(224,224,3)):
    """
    This function performs does two things:
    A - Generates the embedding for an RGB image using the classifier.
    B - Converts the image from a RGB colorspace to Lab colorspace and
        separates the L and ab images
    """
    
    # A - generate embedding
    # 1. Resize for embed and convert to rgb 
    gray = gray2rgb(rgb2gray(rgb))
    gray = batch_apply(gray, resize, embed_size, mode='constant')
    # 2. Zero-Center [-1,1]
    gray = gray * 2 - 1
    # 3. generate embeddings using the classifier
    embed = create_pretrained_embedding(embed_model, gray)
    
    # B - Convert to 'Lab' colorspace and extract 'L' and 'ab channels'
    # Resize to input size of model
    re_batch = batch_apply(rgb, resize, input_size, mode='constant')
    # RGB => L*a*b*
    re_batch = batch_apply(re_batch, rgb2lab)

    # Extract L* into X, zero-center and normalize
    X_batch = re_batch[:,:,:,0]
    X_batch = X_batch/50 - 1
    X_batch = X_batch.reshape(X_batch.shape+(1,))

    # Extract a*b* into Y and normalize. Already zero-centered.
    Y_batch = re_batch[:,:,:,1:]
    Y_batch = Y_batch/128

    return [X_batch, embed], Y_batch


def image_a_b_gen(images, batch_size, embed_model):
    """
    From a large set of images this function preprocces batches of images.
    Preprocessing yields X(gray) and Y(ab channels) for training.
    """
    for batch in datagen.flow(images, batch_size=batch_size):
        yield preprocess_images(batch, embed_model)
        

def train(model, embed_model, training_files, batch_size=20, epochs=500, steps_per_epoch=50):
    """Trains the model"""
    # 1. Generate normalized and resized training files
    training_set = getImages(training_files)
    
    # 2. Define the size of training files -> 85% of the training set
    train_size = int(len(training_set)*0.85)
    
    # 3. Slice the file list to the training size defined above
    train_images = training_set[:train_size]
    
    # 4. Set the remaining images as validation data
    val_images = training_set[train_size:]
    
    # 5. Define validation step
    val_steps = (len(val_images)//batch_size)
    
    print("Training samples:", train_size, "Validation samples:", len(val_images))
    
    # 6. List necessary callbacks needed during training
    log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
    callbacks = [
        EarlyStopping(monitor='val_loss', patience=15, verbose=1, min_delta=1e-5),
        ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, cooldown=0, verbose=1, min_lr=1e-8),
        ModelCheckpoint(monitor='val_loss', filepath='model/colorize.hdf5', verbose=1,
                         save_best_only=True, save_weights_only=True, mode='auto'),
        TensorBoard(log_dir='/logs/fit/', histogram_freq=1, batch_size=20, write_graph=True, write_grads=True,
                    write_images=False, embeddings_freq=0),
        CSVLogger("/logs/training.log",separator=",", append=False)
    ]
    # Fit the model on batches on preprocessed train images
    model.fit_generator(image_a_b_gen(train_images, batch_size, embed_model), epochs=epochs, steps_per_epoch=steps_per_epoch,
                        verbose=1, callbacks=callbacks, validation_data=preprocess_images(val_images, embed_model))

        
def batch_apply(ndarray, func, *args, **kwargs):
    """Calls func with samples, func should take ndarray as first positional argument"""

    batch = []
    for sample in ndarray:
        batch.append(func(sample, *args, **kwargs))
    return np.array(batch)

def create_pretrained_embedding(embed_model, rgb):
    """
    This function uses the pretrained CRC classifier and 
    returns the embedding that predicts the tissue class 
    for a given RGB image
    """
    return embed_model.predict(rgb)

def plot_performance(hist, model_name):
    """
    Plots the accuracy and loss during the training process.
    Args: 
    history: variable assigned to model.fit()
    model_name: A string identifying the model
    """
    fig, (ax1,ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,6))
    fig.suptitle("{} Training".format(model_name), fontsize=20)

    max_epoch = len(hist.history['accuracy'])+1
    epochs_list = list(range(1, max_epoch))

    ax1.plot(epochs_list, hist.history['accuracy'], color='b', linestyle='-', label='Training Data')
    ax1.plot(epochs_list, hist.history['val_accuracy'], color='r', linestyle='-', label ='Validation Data')
    ax1.set_title('Accuracy', fontsize=14)
    ax1.set_xlabel('Epochs', fontsize=14)
    ax1.set_ylabel('Accuracy', fontsize=14)
    ax1.legend(frameon=False, loc='lower center', ncol=2)

    ax2.plot(epochs_list, hist.history['loss'], color='b', linestyle='-', label='Training Data')
    ax2.plot(epochs_list, hist.history['val_loss'], color='r', linestyle='-', label ='Validation Data')
    ax2.set_title('Loss', fontsize=14)
    ax2.set_xlabel('Epochs', fontsize=14)
    ax2.set_ylabel('Loss', fontsize=14)
    ax2.legend(frameon=False, loc='upper center', ncol=2)
    
    if figure_directory:
        plt.savefig(figures+"/history")
    plt.show()

!pwd

/Users/rajesharasada/Documents/DataScience/neural_colorization-workspace

pretrained_weights = '../models/vgg19_model_finetuned.h5'

my_model = Colorization_Model()

train_dir = '/Users/rajesharasada/Documents/DataScience/neural_colorization-workspace/input/crc/NCT-CRC-HE-100K'
os.listdir(train_dir)

DATASET = []
for folder in os.listdir(train_dir):
    if folder != ".DS_Store":
        subfolder = os.path.join(train_dir,folder)
        for fname in os.listdir(subfolder):
            #print(fname)
            DATASET.append(os.path.join(subfolder, fname))

DATASET[:5]

['/floyd/input/crc/NCT-CRC-HE-100K/TUM/TUM-RVIQKNST.tif',
 '/floyd/input/crc/NCT-CRC-HE-100K/TUM/TUM-HKQPGCSW.tif',
 '/floyd/input/crc/NCT-CRC-HE-100K/TUM/TUM-DDGVVTHE.tif',
 '/floyd/input/crc/NCT-CRC-HE-100K/TUM/TUM-EFFWDMPA.tif',
 '/floyd/input/crc/NCT-CRC-HE-100K/TUM/TUM-FMRFGRIT.tif']

# Shuffle the files
dataset = shuffle(DATASET)

# Split the dataset into training and testing files
# The entire dataset with 100000 images is too large
# Hence only 40,000 images were used
training_files, testing_files = train_test_split(dataset[:40000], test_size=0.15)

# Length of the training set
len(training_files)

34000

# Images were augmented
datagen = ImageDataGenerator(shear_range=0.2,
                             zoom_range=0.2,
                             rotation_range=20,
                             horizontal_flip=True)

embed_model = load_pretrained_vgg19_embedding(pretrained_weights)

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5
80142336/80134624 [==============================] - 1s 0us/step
Model Compiled
Loaded Finetuned Weights

batch_size=50

history = train(model = my_model, embed_model = embed_model, training_files = training_files, epochs=100)

100%|██████████| 34000/34000 [07:18<00:00, 77.48it/s] 

Training samples: 28900 Validation samples: 5100
WARNING:tensorflow:From /floyd/home/utils.py:217: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
Please use Model.fit, which supports generators.
Epoch 1/100
50/50 [==============================] - ETA: 0s - loss: 0.8741 - root_mean_squared_error: 0.9349
Epoch 00001: val_loss improved from inf to 0.69528, saving model to model/colorize.hdf5
50/50 [==============================] - 86s 2s/step - loss: 0.8741 - root_mean_squared_error: 0.9349 - val_loss: 0.6953 - val_root_mean_squared_error: 0.8338 - lr: 0.0010
Epoch 2/100
50/50 [==============================] - ETA: 0s - loss: 0.7531 - root_mean_squared_error: 0.8678
Epoch 00002: val_loss improved from 0.69528 to 0.65983, saving model to model/colorize.hdf5
50/50 [==============================] - 82s 2s/step - loss: 0.7531 - root_mean_squared_error: 0.8678 - val_loss: 0.6598 - val_root_mean_squared_error: 0.8123 - lr: 0.0010
Epoch 3/100
50/50 [==============================] - ETA: 0s - loss: 0.6630 - root_mean_squared_error: 0.8142
Epoch 00003: val_loss did not improve from 0.65983
50/50 [==============================] - 82s 2s/step - loss: 0.6630 - root_mean_squared_error: 0.8142 - val_loss: 0.6714 - val_root_mean_squared_error: 0.8194 - lr: 0.0010
Epoch 4/100
50/50 [==============================] - ETA: 0s - loss: 0.5793 - root_mean_squared_error: 0.7611
Epoch 00004: val_loss improved from 0.65983 to 0.53459, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.5793 - root_mean_squared_error: 0.7611 - val_loss: 0.5346 - val_root_mean_squared_error: 0.7312 - lr: 0.0010
Epoch 5/100
50/50 [==============================] - ETA: 0s - loss: 0.5032 - root_mean_squared_error: 0.7093
Epoch 00005: val_loss improved from 0.53459 to 0.43832, saving model to model/colorize.hdf5
50/50 [==============================] - 82s 2s/step - loss: 0.5032 - root_mean_squared_error: 0.7093 - val_loss: 0.4383 - val_root_mean_squared_error: 0.6621 - lr: 0.0010
Epoch 6/100
50/50 [==============================] - ETA: 0s - loss: 0.4290 - root_mean_squared_error: 0.6550
Epoch 00006: val_loss did not improve from 0.43832
50/50 [==============================] - 83s 2s/step - loss: 0.4290 - root_mean_squared_error: 0.6550 - val_loss: 1.1909 - val_root_mean_squared_error: 1.0913 - lr: 0.0010
Epoch 7/100
50/50 [==============================] - ETA: 0s - loss: 0.3637 - root_mean_squared_error: 0.6031
Epoch 00007: val_loss improved from 0.43832 to 0.20486, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.3637 - root_mean_squared_error: 0.6031 - val_loss: 0.2049 - val_root_mean_squared_error: 0.4526 - lr: 0.0010
Epoch 8/100
50/50 [==============================] - ETA: 0s - loss: 0.2990 - root_mean_squared_error: 0.5468
Epoch 00008: val_loss did not improve from 0.20486
50/50 [==============================] - 82s 2s/step - loss: 0.2990 - root_mean_squared_error: 0.5468 - val_loss: 0.5918 - val_root_mean_squared_error: 0.7693 - lr: 0.0010
Epoch 9/100
50/50 [==============================] - ETA: 0s - loss: 0.2299 - root_mean_squared_error: 0.4795
Epoch 00009: val_loss did not improve from 0.20486
50/50 [==============================] - 83s 2s/step - loss: 0.2299 - root_mean_squared_error: 0.4795 - val_loss: 0.2945 - val_root_mean_squared_error: 0.5427 - lr: 0.0010
Epoch 10/100
50/50 [==============================] - ETA: 0s - loss: 0.1710 - root_mean_squared_error: 0.4135
Epoch 00010: val_loss did not improve from 0.20486
50/50 [==============================] - 83s 2s/step - loss: 0.1710 - root_mean_squared_error: 0.4135 - val_loss: 0.3275 - val_root_mean_squared_error: 0.5723 - lr: 0.0010
Epoch 11/100
50/50 [==============================] - ETA: 0s - loss: 0.1193 - root_mean_squared_error: 0.3453
Epoch 00011: val_loss did not improve from 0.20486
50/50 [==============================] - 83s 2s/step - loss: 0.1193 - root_mean_squared_error: 0.3453 - val_loss: 0.3172 - val_root_mean_squared_error: 0.5632 - lr: 0.0010
Epoch 12/100
50/50 [==============================] - ETA: 0s - loss: 0.0833 - root_mean_squared_error: 0.2887
Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00012: val_loss did not improve from 0.20486
50/50 [==============================] - 82s 2s/step - loss: 0.0833 - root_mean_squared_error: 0.2887 - val_loss: 0.3580 - val_root_mean_squared_error: 0.5983 - lr: 0.0010
Epoch 13/100
50/50 [==============================] - ETA: 0s - loss: 0.0544 - root_mean_squared_error: 0.2332
Epoch 00013: val_loss did not improve from 0.20486
50/50 [==============================] - 83s 2s/step - loss: 0.0544 - root_mean_squared_error: 0.2332 - val_loss: 0.2208 - val_root_mean_squared_error: 0.4699 - lr: 1.0000e-04
Epoch 14/100
50/50 [==============================] - ETA: 0s - loss: 0.0521 - root_mean_squared_error: 0.2284
Epoch 00014: val_loss improved from 0.20486 to 0.14223, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0521 - root_mean_squared_error: 0.2284 - val_loss: 0.1422 - val_root_mean_squared_error: 0.3771 - lr: 1.0000e-04
Epoch 15/100
50/50 [==============================] - ETA: 0s - loss: 0.0498 - root_mean_squared_error: 0.2231
Epoch 00015: val_loss improved from 0.14223 to 0.10030, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0498 - root_mean_squared_error: 0.2231 - val_loss: 0.1003 - val_root_mean_squared_error: 0.3167 - lr: 1.0000e-04
Epoch 16/100
50/50 [==============================] - ETA: 0s - loss: 0.0485 - root_mean_squared_error: 0.2203
Epoch 00016: val_loss improved from 0.10030 to 0.07037, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0485 - root_mean_squared_error: 0.2203 - val_loss: 0.0704 - val_root_mean_squared_error: 0.2653 - lr: 1.0000e-04
Epoch 17/100
50/50 [==============================] - ETA: 0s - loss: 0.0467 - root_mean_squared_error: 0.2160
Epoch 00017: val_loss improved from 0.07037 to 0.06375, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0467 - root_mean_squared_error: 0.2160 - val_loss: 0.0637 - val_root_mean_squared_error: 0.2525 - lr: 1.0000e-04
Epoch 18/100
50/50 [==============================] - ETA: 0s - loss: 0.0450 - root_mean_squared_error: 0.2121
Epoch 00018: val_loss improved from 0.06375 to 0.04642, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0450 - root_mean_squared_error: 0.2121 - val_loss: 0.0464 - val_root_mean_squared_error: 0.2155 - lr: 1.0000e-04
Epoch 19/100
50/50 [==============================] - ETA: 0s - loss: 0.0431 - root_mean_squared_error: 0.2076
Epoch 00019: val_loss improved from 0.04642 to 0.04114, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0431 - root_mean_squared_error: 0.2076 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028 - lr: 1.0000e-04
Epoch 20/100
50/50 [==============================] - ETA: 0s - loss: 0.0415 - root_mean_squared_error: 0.2036
Epoch 00020: val_loss improved from 0.04114 to 0.03822, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0415 - root_mean_squared_error: 0.2036 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955 - lr: 1.0000e-04
Epoch 21/100
50/50 [==============================] - ETA: 0s - loss: 0.0404 - root_mean_squared_error: 0.2010
Epoch 00021: val_loss did not improve from 0.03822
50/50 [==============================] - 83s 2s/step - loss: 0.0404 - root_mean_squared_error: 0.2010 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960 - lr: 1.0000e-04
Epoch 22/100
50/50 [==============================] - ETA: 0s - loss: 0.0395 - root_mean_squared_error: 0.1988
Epoch 00022: val_loss improved from 0.03822 to 0.03227, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0395 - root_mean_squared_error: 0.1988 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1796 - lr: 1.0000e-04
Epoch 23/100
50/50 [==============================] - ETA: 0s - loss: 0.0367 - root_mean_squared_error: 0.1916
Epoch 00023: val_loss improved from 0.03227 to 0.02781, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0367 - root_mean_squared_error: 0.1916 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1668 - lr: 1.0000e-04
Epoch 24/100
50/50 [==============================] - ETA: 0s - loss: 0.0360 - root_mean_squared_error: 0.1898
Epoch 00024: val_loss did not improve from 0.02781
50/50 [==============================] - 82s 2s/step - loss: 0.0360 - root_mean_squared_error: 0.1898 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1758 - lr: 1.0000e-04
Epoch 25/100
50/50 [==============================] - ETA: 0s - loss: 0.0352 - root_mean_squared_error: 0.1876
Epoch 00025: val_loss did not improve from 0.02781
50/50 [==============================] - 83s 2s/step - loss: 0.0352 - root_mean_squared_error: 0.1876 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1727 - lr: 1.0000e-04
Epoch 26/100
50/50 [==============================] - ETA: 0s - loss: 0.0326 - root_mean_squared_error: 0.1806
Epoch 00026: val_loss did not improve from 0.02781
50/50 [==============================] - 82s 2s/step - loss: 0.0326 - root_mean_squared_error: 0.1806 - val_loss: 0.0285 - val_root_mean_squared_error: 0.1689 - lr: 1.0000e-04
Epoch 27/100
50/50 [==============================] - ETA: 0s - loss: 0.0326 - root_mean_squared_error: 0.1806
Epoch 00027: val_loss improved from 0.02781 to 0.02336, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0326 - root_mean_squared_error: 0.1806 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1528 - lr: 1.0000e-04
Epoch 28/100
50/50 [==============================] - ETA: 0s - loss: 0.0307 - root_mean_squared_error: 0.1753
Epoch 00028: val_loss did not improve from 0.02336
50/50 [==============================] - 82s 2s/step - loss: 0.0307 - root_mean_squared_error: 0.1753 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1740 - lr: 1.0000e-04
Epoch 29/100
50/50 [==============================] - ETA: 0s - loss: 0.0296 - root_mean_squared_error: 0.1721
Epoch 00029: val_loss did not improve from 0.02336
50/50 [==============================] - 83s 2s/step - loss: 0.0296 - root_mean_squared_error: 0.1721 - val_loss: 0.0530 - val_root_mean_squared_error: 0.2303 - lr: 1.0000e-04
Epoch 30/100
50/50 [==============================] - ETA: 0s - loss: 0.0284 - root_mean_squared_error: 0.1687
Epoch 00030: val_loss did not improve from 0.02336
50/50 [==============================] - 82s 2s/step - loss: 0.0284 - root_mean_squared_error: 0.1687 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1537 - lr: 1.0000e-04
Epoch 31/100
50/50 [==============================] - ETA: 0s - loss: 0.0269 - root_mean_squared_error: 0.1640
Epoch 00031: val_loss did not improve from 0.02336
50/50 [==============================] - 82s 2s/step - loss: 0.0269 - root_mean_squared_error: 0.1640 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1555 - lr: 1.0000e-04
Epoch 32/100
50/50 [==============================] - ETA: 0s - loss: 0.0259 - root_mean_squared_error: 0.1610
Epoch 00032: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00032: val_loss did not improve from 0.02336
50/50 [==============================] - 83s 2s/step - loss: 0.0259 - root_mean_squared_error: 0.1610 - val_loss: 0.0631 - val_root_mean_squared_error: 0.2512 - lr: 1.0000e-04
Epoch 33/100
50/50 [==============================] - ETA: 0s - loss: 0.0256 - root_mean_squared_error: 0.1601
Epoch 00033: val_loss improved from 0.02336 to 0.02317, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0256 - root_mean_squared_error: 0.1601 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1522 - lr: 1.0000e-05
Epoch 34/100
50/50 [==============================] - ETA: 0s - loss: 0.0256 - root_mean_squared_error: 0.1599
Epoch 00034: val_loss improved from 0.02317 to 0.02300, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0256 - root_mean_squared_error: 0.1599 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1517 - lr: 1.0000e-05
Epoch 35/100
50/50 [==============================] - ETA: 0s - loss: 0.0250 - root_mean_squared_error: 0.1582
Epoch 00035: val_loss did not improve from 0.02300
50/50 [==============================] - 82s 2s/step - loss: 0.0250 - root_mean_squared_error: 0.1582 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1538 - lr: 1.0000e-05
Epoch 36/100
50/50 [==============================] - ETA: 0s - loss: 0.0251 - root_mean_squared_error: 0.1584
Epoch 00036: val_loss did not improve from 0.02300
50/50 [==============================] - 83s 2s/step - loss: 0.0251 - root_mean_squared_error: 0.1584 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1545 - lr: 1.0000e-05
Epoch 37/100
50/50 [==============================] - ETA: 0s - loss: 0.0251 - root_mean_squared_error: 0.1585
Epoch 00037: val_loss did not improve from 0.02300
50/50 [==============================] - 83s 2s/step - loss: 0.0251 - root_mean_squared_error: 0.1585 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1547 - lr: 1.0000e-05
Epoch 38/100
50/50 [==============================] - ETA: 0s - loss: 0.0247 - root_mean_squared_error: 0.1571
Epoch 00038: val_loss did not improve from 0.02300
50/50 [==============================] - 82s 2s/step - loss: 0.0247 - root_mean_squared_error: 0.1571 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1555 - lr: 1.0000e-05
Epoch 39/100
50/50 [==============================] - ETA: 0s - loss: 0.0244 - root_mean_squared_error: 0.1561
Epoch 00039: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.

Epoch 00039: val_loss did not improve from 0.02300
50/50 [==============================] - 83s 2s/step - loss: 0.0244 - root_mean_squared_error: 0.1561 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1518 - lr: 1.0000e-05
Epoch 40/100
50/50 [==============================] - ETA: 0s - loss: 0.0244 - root_mean_squared_error: 0.1561
Epoch 00040: val_loss did not improve from 0.02300
50/50 [==============================] - 82s 2s/step - loss: 0.0244 - root_mean_squared_error: 0.1561 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1526 - lr: 1.0000e-06
Epoch 41/100
50/50 [==============================] - ETA: 0s - loss: 0.0243 - root_mean_squared_error: 0.1560
Epoch 00041: val_loss did not improve from 0.02300
50/50 [==============================] - 82s 2s/step - loss: 0.0243 - root_mean_squared_error: 0.1560 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1532 - lr: 1.0000e-06
Epoch 42/100
50/50 [==============================] - ETA: 0s - loss: 0.0244 - root_mean_squared_error: 0.1562
Epoch 00042: val_loss did not improve from 0.02300
50/50 [==============================] - 83s 2s/step - loss: 0.0244 - root_mean_squared_error: 0.1562 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1529 - lr: 1.0000e-06
Epoch 43/100
50/50 [==============================] - ETA: 0s - loss: 0.0240 - root_mean_squared_error: 0.1549
Epoch 00043: val_loss improved from 0.02300 to 0.02300, saving model to model/colorize.hdf5
50/50 [==============================] - 83s 2s/step - loss: 0.0240 - root_mean_squared_error: 0.1549 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1517 - lr: 1.0000e-06
Epoch 44/100
50/50 [==============================] - ETA: 0s - loss: 0.0250 - root_mean_squared_error: 0.1581
Epoch 00044: ReduceLROnPlateau reducing learning rate to 1.0000001111620805e-07.

Epoch 00044: val_loss did not improve from 0.02300
50/50 [==============================] - 83s 2s/step - loss: 0.0250 - root_mean_squared_error: 0.1581 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1548 - lr: 1.0000e-06
Epoch 45/100
50/50 [==============================] - ETA: 0s - loss: 0.0246 - root_mean_squared_error: 0.1569
Epoch 00045: val_loss did not improve from 0.02300
50/50 [==============================] - 83s 2s/step - loss: 0.0246 - root_mean_squared_error: 0.1569 - val_loss: 0.0241 - val_root_mean_squared_error: 0.1552 - lr: 1.0000e-07
Epoch 46/100
50/50 [==============================] - ETA: 0s - loss: 0.0245 - root_mean_squared_error: 0.1566
Epoch 00046: val_loss did not improve from 0.02300
50/50 [==============================] - 82s 2s/step - loss: 0.0245 - root_mean_squared_error: 0.1566 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1565 - lr: 1.0000e-07
Epoch 47/100
50/50 [==============================] - ETA: 0s - loss: 0.0248 - root_mean_squared_error: 0.1574
Epoch 00047: val_loss did not improve from 0.02300
50/50 [==============================] - 83s 2s/step - loss: 0.0248 - root_mean_squared_error: 0.1574 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1576 - lr: 1.0000e-07
Epoch 48/100
50/50 [==============================] - ETA: 0s - loss: 0.0243 - root_mean_squared_error: 0.1558
Epoch 00048: val_loss did not improve from 0.02300
50/50 [==============================] - 82s 2s/step - loss: 0.0243 - root_mean_squared_error: 0.1558 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1568 - lr: 1.0000e-07
Epoch 49/100
50/50 [==============================] - ETA: 0s - loss: 0.0244 - root_mean_squared_error: 0.1563
Epoch 00049: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-08.

Epoch 00049: val_loss did not improve from 0.02300
50/50 [==============================] - 83s 2s/step - loss: 0.0244 - root_mean_squared_error: 0.1563 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1557 - lr: 1.0000e-07
Epoch 00049: early stopping

def test(model, embed_model, testing_files, save_actual=False, save_gray=False):
    print("Started")
    test_images = getImages(testing_files)
    
    model.load_weights(filepath='model/colorize.hdf5')

    print('Preprocessing Images')
    X_test, Y_test = preprocess_images(test_images, embed_model)
    
    print('Predicting')
    # Test model
    output = model.predict(X_test)
    
    # Rescale a*b* back. [-1.0, 1.0] => [-128.0, 128.0]
    output = output * 128
    Y_test = Y_test * 128

    # Output colorizations
    for i in range(len(output)):
        name = testing_files[i].split(".")[0].split("/")[-1]
        print('Saving '+str(i)+"th image " + name + ".png")
        
        lightness = X_test[0][i][:,:,0]
        
        #Rescale L* back. [-1.0, 1.0] => [0.0, 100.0]
        lightness = (lightness + 1) * 50
        
        predicted = np.zeros((224, 224, 3))
        predicted[:,:,0] = lightness
        predicted[:,:,1:] = output[i]
        plt.imsave("results/predicted/" + name + ".jpeg", lab2rgb(predicted))
        
        if save_gray:
            bnw = np.zeros((224, 224, 3))
            bnw[:,:,0] = lightness
            plt.imsave("results/bnw/" + name + ".jpeg", lab2rgb(bnw))
        
        if save_actual:
            actual = np.zeros((224, 224, 3))
            actual[:,:,0] = lightness
            actual[:,:,1:] = Y_test[i]
            plt.imsave("results/actual/" + name + ".jpeg", lab2rgb(actual))

test(my_model, embed_model, testing_files, True, True)

  0%|          | 18/6000 [00:00<00:34, 175.27it/s]

Started

100%|██████████| 6000/6000 [00:52<00:00, 114.68it/s]

Preprocessing Images
Predicting
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/usr/local/lib/python3.7/site-packages/skimage/color/colorconv.py:1128: UserWarning: Color data out of range: Z < 0 in 1 pixels
  return xyz2rgb(lab2xyz(lab, illuminant, observer))

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Saving 256th image STR-LPSDERWD*.png
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Saving 258th image BACK-AAPMIHGF*.png
Saving 259th image LYM-CYFKNFAD*.png
Saving 260th image LYM-SYMGNTEE*.png
Saving 261th image DEB-FIIADTIF*.png
Saving 262th image MUC-NGQHSNQC*.png
Saving 263th image STR-LTVKRQCI*.png
Saving 264th image BACK-IIDQEHFN*.png
Saving 265th image ADI-RDPCEHHP*.png
Saving 266th image TUM-SCDFCRLK*.png
Saving 267th image DEB-LERTKATH*.png
Saving 268th image NORM-DMYYQLMH*.png
Saving 269th image LYM-WTQNHAHP*.png
Saving 270th image LYM-RARNERWW*.png
Saving 271th image STR-DMKIGSQK*.png
Saving 272th image ADI-NFLYSDRN*.png
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Saving 274th image MUC-YHRGEHLP*.png
Saving 275th image LYM-ELDCQVCI*.png
Saving 276th image STR-IPLPCMNP*.png
Saving 277th image ADI-IKCGMWLY*.png
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Saving 284th image MUC-GPLQSPPQ*.png
Saving 285th image BACK-MKTMANNN*.png
Saving 286th image DEB-NRRPIKLP*.png
Saving 287th image LYM-CMPPEMHS*.png
Saving 288th image LYM-MCKDDGHL*.png
Saving 289th image MUS-FMRGKPLS*.png
Saving 290th image MUC-LADRCTLT*.png
Saving 291th image TUM-QCVQGRKH*.png
Saving 292th image TUM-CAHQGQCY*.png
Saving 293th image ADI-PVGDMSLR*.png
Saving 294th image MUS-HEVEDMSP*.png
Saving 295th image MUS-LFGHVNGY*.png
Saving 296th image BACK-FGSTGHLD*.png
Saving 297th image MUC-HCNYGWDC*.png
Saving 298th image BACK-EDLMEVFW*.png
Saving 299th image TUM-CMMYTSLL*.png
Saving 300th image LYM-GRDYTWSN*.png
Saving 301th image MUS-DDGEVFTI*.png
Saving 302th image ADI-KTHLFSLM*.png
Saving 303th image TUM-YVGPMAGH*.png
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Saving 305th image DEB-IKSFYHIC*.png
Saving 306th image DEB-YLRKQTCN*.png
Saving 307th image BACK-FYYGMNEI*.png
Saving 308th image MUC-NYKMRGDC*.png
Saving 309th image TUM-PHKVRASN*.png
Saving 310th image BACK-LAYLYNMI*.png
Saving 311th image MUS-TKSYIRYR*.png
Saving 312th image DEB-GETRPFKC*.png
Saving 313th image MUS-MPWDALEW*.png
Saving 314th image TUM-MCVPYTER*.png
Saving 315th image TUM-VTWSILMN*.png
Saving 316th image BACK-NLTKEHVH*.png
Saving 317th image MUC-PETTPLCR*.png
Saving 318th image MUS-NTCSFQFQ*.png
Saving 319th image DEB-FAGGIGLF*.png
Saving 320th image BACK-GDFSIMRD*.png
Saving 321th image DEB-SDTVETLT*.png
Saving 322th image BACK-KDSKQQVV*.png
Saving 323th image MUC-NFMLLMKI*.png
Saving 324th image TUM-DRIFVNKW*.png
Saving 325th image NORM-FIDGVLFD*.png
Saving 326th image STR-YMKNTVLR*.png
Saving 327th image MUC-KDMLNFMG*.png
Saving 328th image STR-FRFWCMCR*.png
Saving 329th image STR-YIMAENRS*.png
Saving 330th image MUS-KMRYLDIH*.png
Saving 331th image STR-HFSPSKDQ*.png
Saving 332th image MUS-CGTVEHLP*.png
Saving 333th image STR-QHEYWSTT*.png
Saving 334th image DEB-GVPLIFSE*.png
Saving 335th image MUC-PSPCYVQC*.png
Saving 336th image LYM-KCNYVEHF*.png
Saving 337th image LYM-WHSVLTSN*.png
Saving 338th image BACK-CTKEAMNW*.png
Saving 339th image TUM-PQMHDAQE*.png
Saving 340th image MUS-AVSMYKQM*.png
Saving 341th image STR-GMFNGATR*.png
Saving 342th image ADI-HRLKQYAP*.png
Saving 343th image DEB-FVDFDHGS*.png
Saving 344th image TUM-QQCEEQSG*.png
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Saving 349th image ADI-LVNMMSIN*.png
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Saving 354th image LYM-DIDKTRPE*.png
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Saving 356th image MUC-VNSYKFSQ*.png
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Saving 394th image MUC-FPRGVFYR*.png
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Saving 399th image LYM-NHPDGYNH*.png
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Saving 409th image STR-DIIRVASP*.png
Saving 410th image ADI-MNPILPLN*.png
Saving 411th image TUM-LTWYPELV*.png
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Saving 413th image STR-YYDSRDKV*.png
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Saving 416th image ADI-FTCCANIV*.png
Saving 417th image NORM-MYWGDLVH*.png
Saving 418th image MUC-GHEWHGPR*.png
Saving 419th image ADI-ITHMMLHF*.png
Saving 420th image NORM-VHTYEDCE*.png
Saving 421th image STR-KQGKPSDE*.png
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Saving 423th image TUM-AKRSIAAQ*.png
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Saving 426th image NORM-EFFRWWFQ*.png
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Saving 428th image TUM-KQYDSWFP*.png
Saving 429th image LYM-WPKDCQWM*.png
Saving 430th image DEB-TLELPHIV*.png
Saving 431th image DEB-TMSVRDNV*.png
Saving 432th image MUC-LFTFKPQV*.png
Saving 433th image NORM-DPFYWTLH*.png
Saving 434th image MUC-PVQQFFYA*.png
Saving 435th image DEB-SMEIPEQI*.png
Saving 436th image MUC-GQCHMPMW*.png
Saving 437th image MUS-KRHHCWGL*.png
Saving 438th image STR-SINISPHQ*.png
Saving 439th image STR-SQLRHYFT*.png
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Saving 441th image STR-SMTGLEES*.png
Saving 442th image NORM-RCASACWF*.png
Saving 443th image LYM-HPTSDHAY*.png
Saving 444th image STR-MYMHKARN*.png
Saving 445th image TUM-WDPYLFNP*.png
Saving 446th image TUM-MWTWEANT*.png
Saving 447th image ADI-NFYECKLG*.png
Saving 448th image DEB-NWRRSMAD*.png
Saving 449th image BACK-NPFRLTGM*.png
Saving 450th image STR-KEVPVKGN*.png
Saving 451th image BACK-GKAWFWDH*.png
Saving 452th image MUS-DCFQSICP*.png
Saving 453th image MUC-CSNDCLSD*.png
Saving 454th image MUC-GVVCMHCK*.png
Saving 455th image NORM-FHNLKNMH*.png
Saving 456th image DEB-VHWRCYTH*.png
Saving 457th image ADI-PPEPHTFI*.png
Saving 458th image DEB-CAYCVMSF*.png
Saving 459th image MUC-DWFWIWFC*.png
Saving 460th image LYM-NMTMAYEL*.png
Saving 461th image STR-QNPEKTNG*.png
Saving 462th image TUM-QAWRWYMM*.png
Saving 463th image BACK-AASIPVGN*.png
Saving 464th image TUM-IVNHISGD*.png
Saving 465th image ADI-RNEEMETY*.png
Saving 466th image DEB-THICEQVM*.png
Saving 467th image ADI-ACEGAGDH*.png
Saving 468th image MUS-QSCIENVF*.png
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Saving 470th image STR-HIDQAPRM*.png
Saving 471th image TUM-WASTYCHS*.png
Saving 472th image ADI-AALYMMMY*.png
Saving 473th image BACK-GSQKIGFH*.png
Saving 474th image NORM-IRVPSDKQ*.png
Saving 475th image DEB-KTGRPEQL*.png
Saving 476th image BACK-FWTKMNLK*.png
Saving 477th image STR-DVEKKRGP*.png
Saving 478th image MUC-GIEAGDDA*.png
Saving 479th image DEB-VQYMECWV*.png
Saving 480th image STR-VAFAWNKA*.png
Saving 481th image ADI-HCWNVLFK*.png
Saving 482th image DEB-DEDRQRSS*.png
Saving 483th image STR-WCYTMCNK*.png
Saving 484th image LYM-TNKLSILF*.png
Saving 485th image MUS-NGIYRKCM*.png
Saving 486th image MUC-ASIKDMVV*.png
Saving 487th image LYM-QKKKIPNS*.png
Saving 488th image TUM-WNQALESY*.png
Saving 489th image TUM-RKCMHNSN*.png
Saving 490th image STR-YSATQARG*.png
Saving 491th image TUM-SAAPMRPH*.png
Saving 492th image MUS-PQEFKIAM*.png
Saving 493th image NORM-WQVAWRSE*.png
Saving 494th image LYM-IQYRQIHQ*.png
Saving 495th image BACK-GVKSRWQQ*.png
Saving 496th image MUS-ATYCTGPW*.png
Saving 497th image DEB-VTSLDGGP*.png
Saving 498th image ADI-EPWDCDMN*.png
Saving 499th image MUS-GWSEKAAR*.png
Saving 500th image LYM-HMEFMFMG*.png
Saving 501th image DEB-KCIRMSGG*.png
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Saving 503th image LYM-YLLFLLND*.png
Saving 504th image TUM-DHLWSPQW*.png
Saving 505th image BACK-LPAMSWHM*.png
Saving 506th image STR-NAGEHKQF*.png
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Saving 508th image TUM-DTSAIEYM*.png
Saving 509th image TUM-EHHFIDPN*.png
Saving 510th image BACK-AYRKYQTC*.png
Saving 511th image NORM-PQFNIDCQ*.png
Saving 512th image TUM-SMEKMRWV*.png
Saving 513th image MUS-QVLCTMYG*.png
Saving 514th image ADI-QWYERSRP*.png
Saving 515th image MUS-HIMLARLA*.png
Saving 516th image TUM-FWPWGGWR*.png
Saving 517th image LYM-PHNMDWVM*.png
Saving 518th image MUS-RDTQAWPA*.png
Saving 519th image MUS-NKTWAVFF*.png
Saving 520th image BACK-FHIPFSAP*.png
Saving 521th image DEB-VVVCMTPV*.png
Saving 522th image DEB-QMNDTQKC*.png
Saving 523th image NORM-KVSKSNFP*.png
Saving 524th image BACK-IPIIIGQT*.png
Saving 525th image BACK-MYQRSQMH*.png
Saving 526th image STR-QHEWTYAE*.png
Saving 527th image BACK-NMTRDWPC*.png
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Saving 529th image BACK-KMSLDDYA*.png
Saving 530th image BACK-MQYGMTEV*.png
Saving 531th image ADI-AECWGELC*.png
Saving 532th image MUS-CRRDEESY*.png
Saving 533th image ADI-MMQGQIQT*.png
Saving 534th image LYM-REKNYEPI*.png
Saving 535th image NORM-KRKHTEYK*.png
Saving 536th image DEB-MPMGVSVC*.png
Saving 537th image BACK-ELGTNISP*.png
Saving 538th image MUS-HRVLPGDS*.png
Saving 539th image NORM-AGAAQNAN*.png
Saving 540th image LYM-CAADDDAS*.png
Saving 541th image ADI-HLKMPIQG*.png
Saving 542th image MUS-LDVDMTST*.png
Saving 543th image MUS-HEAQIGNT*.png
Saving 544th image NORM-SCIYKWLV*.png
Saving 545th image LYM-IVHYSKLC*.png
Saving 546th image MUS-KFKINPQQ*.png
Saving 547th image LYM-CNVKGIIV*.png
Saving 548th image LYM-DMYNEIVR*.png
Saving 549th image TUM-IWLYMWVL*.png
Saving 550th image MUS-EANSADRT*.png
Saving 551th image STR-RYMYRWTY*.png
Saving 552th image DEB-MKGMFARN*.png
Saving 553th image ADI-HMTWWECR*.png
Saving 554th image TUM-TFTENCRF*.png
Saving 555th image TUM-MKEMCLRV*.png
Saving 556th image LYM-CWWAREDT*.png
Saving 557th image NORM-GNDIGDWT*.png
Saving 558th image STR-MRTHNMEG*.png
Saving 559th image DEB-RTPCEAQS*.png
Saving 560th image MUS-QSSQHIMA*.png
Saving 561th image MUC-TEMPWGRP*.png
Saving 562th image MUS-GQSEEWWR*.png
Saving 563th image NORM-WHRAAIFY*.png
Saving 564th image LYM-CVVVVCHY*.png
Saving 565th image LYM-HVCRALSF*.png
Saving 566th image DEB-QIPQKYAP*.png
Saving 567th image DEB-WIFMCAKD*.png
Saving 568th image ADI-ACFFAKLI*.png
Saving 569th image MUC-AWGFMWVV*.png
Saving 570th image LYM-WMWRGVWQ*.png
Saving 571th image LYM-NAKLLQQS*.png
Saving 572th image STR-LTCKDAAK*.png
Saving 573th image TUM-VEMHNECA*.png
Saving 574th image STR-RCNGMLQK*.png
Saving 575th image BACK-DAEQMIRQ*.png
Saving 576th image TUM-LIKPNQRS*.png
Saving 577th image ADI-DVVDYHCN*.png
Saving 578th image ADI-RIATNMVP*.png
Saving 579th image STR-GYGPWVYC*.png
Saving 580th image DEB-LKEVHHDE*.png
Saving 581th image TUM-RYYWMKWI*.png
Saving 582th image NORM-RGQSAKVQ*.png
Saving 583th image BACK-MTKIPHGV*.png
Saving 584th image MUS-TLVLHYYE*.png
Saving 585th image BACK-FCGPVHEN*.png
Saving 586th image ADI-PLAGHNDE*.png
Saving 587th image ADI-ESVYNFTL*.png
Saving 588th image DEB-DTIFITGW*.png
Saving 589th image TUM-ESDACEES*.png
Saving 590th image LYM-KCRQDTYS*.png
Saving 591th image STR-ILQEMGDV*.png
Saving 592th image DEB-TLVQQGIL*.png
Saving 593th image MUS-LTRITFFK*.png
Saving 594th image MUS-GYYNNQFR*.png
Saving 595th image TUM-FLDVCTAW*.png
Saving 596th image MUC-MYESMWCK*.png
Saving 597th image DEB-QDMLFDLG*.png
Saving 598th image LYM-MVMYVTSM*.png
Saving 599th image STR-VDAYEGTA*.png
Saving 600th image LYM-QKVIRPRL*.png
Saving 601th image BACK-GIAKHNRE*.png
Saving 602th image NORM-AEMYDDWI*.png
Saving 603th image DEB-KDHRMTDI*.png
Saving 604th image DEB-QFCPVAAY*.png
Saving 605th image BACK-HQEEWTRI*.png
Saving 606th image BACK-CKQFMMMH*.png
Saving 607th image STR-QVYEYQMQ*.png
Saving 608th image BACK-CVVRKYEV*.png
Saving 609th image DEB-VNKDEQDA*.png
Saving 610th image NORM-NVFMKQKA*.png
Saving 611th image NORM-KGTDYVRE*.png
Saving 612th image NORM-PNFETRAE*.png
Saving 613th image TUM-AVFQAKIA*.png
Saving 614th image LYM-YFSPCGTD*.png
Saving 615th image DEB-FCWITHEL*.png
Saving 616th image NORM-WWMGSRIG*.png
Saving 617th image STR-MPFIFWKD*.png
Saving 618th image DEB-FLCNLFAN*.png
Saving 619th image DEB-VFMKEYDQ*.png
Saving 620th image LYM-HNHPAWST*.png
Saving 621th image STR-INCPQDPT*.png
Saving 622th image MUS-TMLHDHKY*.png
Saving 623th image ADI-EDACMRPC*.png
Saving 624th image LYM-QLDEPFHH*.png
Saving 625th image MUS-HEYCQTLN*.png
Saving 626th image MUS-DNFPKMPQ*.png
Saving 627th image STR-HAHKHIDA*.png
Saving 628th image MUS-SFGKYNFC*.png
Saving 629th image TUM-SAHKGMCE*.png
Saving 630th image MUS-KNITFDDN*.png
Saving 631th image DEB-SGLRMQPT*.png
Saving 632th image ADI-DKMQDLQR*.png
Saving 633th image TUM-KLAKWICE*.png
Saving 634th image BACK-GPMEFDYE*.png
Saving 635th image DEB-SFIPYRPH*.png
Saving 636th image NORM-PFVEQKIE*.png
Saving 637th image STR-GDFEGQVK*.png
Saving 638th image NORM-VFDPTSMI*.png
Saving 639th image DEB-HIFRTWCV*.png
Saving 640th image BACK-AFKMNIGG*.png
Saving 641th image TUM-NVKLPVTT*.png
Saving 642th image ADI-CHVTFDWP*.png
Saving 643th image NORM-EHKDASHC*.png
Saving 644th image LYM-INNIPYFL*.png
Saving 645th image BACK-DEFQPQHK*.png
Saving 646th image LYM-EWKMTYTW*.png
Saving 647th image DEB-QCDMKFGK*.png
Saving 648th image DEB-WWIITFQE*.png
Saving 649th image DEB-DWQLDWFL*.png
Saving 650th image ADI-ARCDKWVP*.png
Saving 651th image MUS-ISPTNWFG*.png
Saving 652th image LYM-CLWFIGDW*.png
Saving 653th image ADI-RYNDPFGR*.png
Saving 654th image TUM-LWMFAAED*.png
Saving 655th image MUS-QDKSMDAD*.png
Saving 656th image MUC-RPSGMSMG*.png
Saving 657th image TUM-GWRGMFEC*.png
Saving 658th image BACK-CKNVIAFE*.png
Saving 659th image TUM-RSGIAYEP*.png
Saving 660th image NORM-CIGAQGHQ*.png
Saving 661th image ADI-KIAILLWD*.png
Saving 662th image BACK-KYSMYWWR*.png
Saving 663th image MUS-ERQIMIQD*.png
Saving 664th image ADI-QGMHDHVC*.png
Saving 665th image NORM-RLRAQFWH*.png
Saving 666th image NORM-ANYLNPVW*.png
Saving 667th image LYM-KREAIRLG*.png
Saving 668th image DEB-LDMQFCRR*.png
Saving 669th image TUM-FQESVVKG*.png
Saving 670th image LYM-NNFWLTDH*.png
Saving 671th image DEB-FCNILWPH*.png
Saving 672th image STR-GCFDDKPC*.png
Saving 673th image MUC-VAIMINGP*.png
Saving 674th image STR-KDIRDLHI*.png
Saving 675th image MUC-KGIGEGEW*.png
Saving 676th image DEB-KMGGRIWA*.png
Saving 677th image ADI-KEMSCVDF*.png
Saving 678th image BACK-ATQMRTGS*.png
Saving 679th image DEB-QGPYQYNH*.png
Saving 680th image NORM-MSFVGGRM*.png
Saving 681th image ADI-IGHEYYPR*.png
Saving 682th image NORM-DFQRSTHD*.png
Saving 683th image ADI-DNLARWMG*.png
Saving 684th image NORM-TWGCRHFS*.png
Saving 685th image BACK-GVRAAWPV*.png
Saving 686th image STR-WARQDDIY*.png
Saving 687th image LYM-AWWRDDEF*.png
Saving 688th image STR-EQRGGVVF*.png
Saving 689th image TUM-DKGAAAFG*.png
Saving 690th image DEB-EQVEYCFV*.png
Saving 691th image MUS-FMQKQKYV*.png
Saving 692th image MUS-PESLASQH*.png
Saving 693th image STR-QKAAFGLH*.png
Saving 694th image TUM-TTGVSCSL*.png
Saving 695th image TUM-AVELCHTA*.png
Saving 696th image BACK-EKVGYEEC*.png
Saving 697th image MUS-GVIVYCRY*.png
Saving 698th image ADI-NPNYSNKV*.png
Saving 699th image ADI-LYFQIFMR*.png
Saving 700th image TUM-WCFHSMFS*.png
Saving 701th image DEB-YMHAPRCD*.png
Saving 702th image TUM-WWLCGCIV*.png
Saving 703th image LYM-QRNQVQEV*.png
Saving 704th image NORM-WVMQHNED*.png
Saving 705th image DEB-PLLRHWFR*.png
Saving 706th image DEB-GEGPHGHC*.png
Saving 707th image TUM-GIVISYCV*.png
Saving 708th image MUS-ELKRNGYW*.png
Saving 709th image ADI-EFISYSRE*.png
Saving 710th image BACK-CHYQVGPP*.png
Saving 711th image BACK-FRFSHWMD*.png
Saving 712th image ADI-IALRGDNI*.png
Saving 713th image TUM-FPMQPESL*.png
Saving 714th image LYM-PCGFGNWL*.png
Saving 715th image NORM-HKEAKVYC*.png
Saving 716th image ADI-KVRRVHFH*.png
Saving 717th image MUC-WEFIRHLW*.png
Saving 718th image BACK-PAQIHIIN*.png
Saving 719th image NORM-MHAGHLLL*.png
Saving 720th image ADI-ENQNPLRK*.png
Saving 721th image MUC-FLYPQPGL*.png
Saving 722th image TUM-DYVIKKSG*.png
Saving 723th image MUC-CVYDSPIW*.png
Saving 724th image TUM-CPHEIGMI*.png
Saving 725th image TUM-FCDYWGTM*.png
Saving 726th image ADI-NQWSFKLG*.png
Saving 727th image MUS-IGGVVMKF*.png
Saving 728th image DEB-SHYWPYMK*.png
Saving 729th image NORM-EMEKKPMD*.png
Saving 730th image ADI-GEYQCTIW*.png
Saving 731th image DEB-SCRDLYHC*.png
Saving 732th image DEB-WGHILRTA*.png
Saving 733th image LYM-GCDGTTSP*.png
Saving 734th image MUS-IIFLQKKA*.png
Saving 735th image LYM-INYDFMEG*.png
Saving 736th image ADI-QDPATHTV*.png
Saving 737th image DEB-KSHGGCCC*.png
Saving 738th image DEB-WAIHKIRP*.png
Saving 739th image MUS-LKCLKFGM*.png
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Saving 775th image ADI-FIGLMDTQ*.png
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Saving 777th image TUM-WSWGPCTF*.png
Saving 778th image BACK-CTCYWSMP*.png
Saving 779th image ADI-CDYQDHYD*.png
Saving 780th image BACK-KWKGDTVT*.png
Saving 781th image LYM-RHDPWQRS*.png
Saving 782th image MUC-MATPNHFC*.png
Saving 783th image TUM-IGLADFYW*.png
Saving 784th image TUM-ANSWCHWY*.png
Saving 785th image MUC-GPPFKSFQ*.png
Saving 786th image ADI-QCKMHCYR*.png
Saving 787th image BACK-FPYNMMLV*.png
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Saving 789th image LYM-MLRDSAGN*.png
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Saving 793th image ADI-QHWSAALL*.png
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Saving 798th image BACK-CPKLGVFF*.png
Saving 799th image ADI-HPFYKRHE*.png
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Saving 802th image STR-EIHVYYRQ*.png
Saving 803th image NORM-MLMPKDKC*.png
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Saving 805th image TUM-LMACHICI*.png
Saving 806th image MUC-WDWWPARP*.png
Saving 807th image LYM-KHRWWGCA*.png
Saving 808th image LYM-HFTKCVLA*.png
Saving 809th image MUC-VDKQQSIE*.png
Saving 810th image BACK-NLGCDLPH*.png
Saving 811th image LYM-YITSCMEY*.png
Saving 812th image TUM-QRTAQWQV*.png
Saving 813th image MUC-RSTTGHFY*.png
Saving 814th image DEB-VIKVMEHK*.png
Saving 815th image ADI-QGSHRALS*.png
Saving 816th image BACK-FQAARVPH*.png
Saving 817th image MUS-VAEWDRSF*.png
Saving 818th image STR-DFPYCGKG*.png
Saving 819th image TUM-QGQTCNHQ*.png
Saving 820th image TUM-IEPEMIQW*.png
Saving 821th image MUC-VERAMGVA*.png
Saving 822th image TUM-WRLDMTPS*.png
Saving 823th image ADI-MSAFHSGD*.png
Saving 824th image STR-ICAYHLGN*.png
Saving 825th image BACK-HDWHYWIK*.png
Saving 826th image MUC-NWESGSKY*.png
Saving 827th image LYM-NMAKVMQM*.png
Saving 828th image NORM-EHHTFMGV*.png
Saving 829th image BACK-IWFWAELP*.png
Saving 830th image BACK-IAQLFKNM*.png
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Saving 833th image STR-EGIKSVCM*.png
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Saving 835th image STR-RNRWIFRD*.png
Saving 836th image LYM-TIKVLVSM*.png
Saving 837th image NORM-PFMEFMSF*.png
Saving 838th image MUC-VINDMHDI*.png
Saving 839th image NORM-TRYMRWHH*.png
Saving 840th image BACK-ICPNRYAQ*.png
Saving 841th image LYM-ANPPRRIM*.png
Saving 842th image DEB-FSGLEWRD*.png
Saving 843th image MUS-RKGHLVAL*.png
Saving 844th image MUC-NLQIHEAE*.png
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Saving 847th image DEB-ENHAATIQ*.png
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/usr/local/lib/python3.7/site-packages/skimage/color/colorconv.py:1128: UserWarning: Color data out of range: Z < 0 in 2 pixels
  return xyz2rgb(lab2xyz(lab, illuminant, observer))

Saving 855th image MUC-NQQNPEKV*.png
Saving 856th image DEB-TYGTGCMN*.png
Saving 857th image STR-LDLRWKMT*.png
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Saving 884th image TUM-ENRFRCDV*.png
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Saving 900th image TUM-NMTMIGYT*.png
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Saving 903th image MUC-EWIRHIRS*.png
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Saving 1130th image BACK-ADHGLQCH*.png

/usr/local/lib/python3.7/site-packages/skimage/color/colorconv.py:1128: UserWarning: Color data out of range: Z < 0 in 5 pixels
  return xyz2rgb(lab2xyz(lab, illuminant, observer))

Saving 1131th image MUS-APFAWFGD*.png
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Saving 1154th image NORM-QRIWALDG*.png
Saving 1155th image MUC-RYRPCIIR*.png
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Saving 1158th image BACK-GSCPNKCA*.png
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Saving 1160th image BACK-CSAFPPGG*.png
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Saving 1162th image STR-YDVANGCC*.png
Saving 1163th image LYM-QMSHTNWV*.png
Saving 1164th image ADI-QYQPTDAT*.png
Saving 1165th image MUC-WVFCFFEW*.png
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Saving 1179th image NORM-GMCRPRNW*.png
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Saving 1184th image ADI-RFQAKANG*.png
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Saving 1187th image TUM-VCRTAQED*.png
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Saving 1189th image LYM-INREACKD*.png
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Saving 2499th image ADI-PADYKHNP*.png
Saving 2500th image LYM-MDQNHNSW*.png
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Saving 2537th image LYM-SCSANCTT*.png
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Saving 2557th image TUM-DDTVRTEN*.png
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Saving 2563th image ADI-FQYFREIK*.png
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Saving 2567th image NORM-ADPMQGDR*.png
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Saving 2570th image MUC-TWITCAYD*.png
Saving 2571th image TUM-KRTDWGGR*.png
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Saving 2576th image TUM-AQQPVIWN*.png
Saving 2577th image LYM-EPRLYKVI*.png
Saving 2578th image MUC-EAAQTTTP*.png
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Saving 2598th image LYM-ICTPNDEC*.png
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Saving 2600th image NORM-DHAYPETI*.png
Saving 2601th image NORM-AHYSVEPY*.png
Saving 2602th image ADI-LYRVRESM*.png
Saving 2603th image DEB-IKVACTWM*.png
Saving 2604th image LYM-SQKSSDMF*.png
Saving 2605th image BACK-EQGNGIEI*.png
Saving 2606th image MUC-YALARSGA*.png
Saving 2607th image DEB-CLECFETF*.png
Saving 2608th image LYM-VQEQYTCK*.png
Saving 2609th image TUM-WYCQTCTV*.png
Saving 2610th image TUM-PRIRHMRK*.png
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Saving 3512th image ADI-MFVGGPNR*.png
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Saving 3534th image DEB-YRFNDLGN*.png
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Saving 3542th image DEB-IQEWKCGQ*.png
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Saving 3614th image MUC-PGYNNWEG*.png
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Saving 3941th image ADI-APTQHMKE*.png
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Saving 3948th image TUM-LLVKVESH*.png
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Saving 3953th image ADI-CNTTFHGN*.png
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Saving 3957th image TUM-QAPRMDWA*.png
Saving 3958th image TUM-TWTDETST*.png
Saving 3959th image LYM-IDMIQINW*.png
Saving 3960th image ADI-PTCDVTCP*.png
Saving 3961th image MUC-DKYYNQIV*.png
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Saving 3979th image TUM-YSDMWCLN*.png
Saving 3980th image TUM-VTLADICY*.png
Saving 3981th image DEB-TLENTLAT*.png
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Saving 3983th image NORM-PCLNRRVP*.png
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Saving 3985th image DEB-SYWSCASD*.png
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Saving 3988th image LYM-HWQPLWPC*.png
Saving 3989th image LYM-IITSDDCR*.png
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Saving 3992th image ADI-DVIRWAWT*.png
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Saving 4000th image NORM-RMQFMWTE*.png
Saving 4001th image TUM-EMIIQFVS*.png
Saving 4002th image STR-MGGVKPWI*.png
Saving 4003th image DEB-MNLMGPIF*.png
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Saving 4008th image DEB-RMTIDVQK*.png
Saving 4009th image ADI-GEVRTIDP*.png
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Saving 4042th image STR-WKVWLCYW*.png
Saving 4043th image NORM-QQKSVGGA*.png
Saving 4044th image ADI-GSDLMPEQ*.png
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Saving 4047th image TUM-MNEPIKPV*.png
Saving 4048th image ADI-GPEYTSQW*.png
Saving 4049th image DEB-AHGLIFQV*.png
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Saving 4052th image TUM-CNAVWEQH*.png
Saving 4053th image DEB-GYPPLMGV*.png
Saving 4054th image LYM-CQEISTSA*.png
Saving 4055th image DEB-IAHCTGVQ*.png
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Saving 4057th image ADI-IGERGCIP*.png
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Saving 4067th image TUM-VFYAFEDY*.png
Saving 4068th image BACK-ALIERLYW*.png
Saving 4069th image DEB-ELSKTFDD*.png
Saving 4070th image TUM-HDEDDYST*.png
Saving 4071th image MUC-AMQNTLRN*.png
Saving 4072th image BACK-MQGRDRYP*.png
Saving 4073th image NORM-ADEQFCTF*.png
Saving 4074th image LYM-PGVCNRRG*.png
Saving 4075th image MUS-QIGLTACV*.png
Saving 4076th image MUC-GNDTANHV*.png
Saving 4077th image MUS-INDLMPYN*.png
Saving 4078th image TUM-IWPDCGET*.png
Saving 4079th image DEB-ESCTGFQK*.png
Saving 4080th image MUC-VQDWDFMW*.png
Saving 4081th image TUM-LVDLNGVW*.png
Saving 4082th image MUS-QKCGRMKQ*.png
Saving 4083th image MUC-KIAEHQDD*.png
Saving 4084th image MUC-HFYSPLMF*.png
Saving 4085th image MUS-CRLHCSIL*.png
Saving 4086th image ADI-MLKVMWIA*.png
Saving 4087th image STR-KFMYLYYM*.png
Saving 4088th image BACK-IAPRSDMS*.png
Saving 4089th image TUM-FVNTEMAM*.png
Saving 4090th image ADI-RASFNSRH*.png
Saving 4091th image LYM-VHTRQWMK*.png
Saving 4092th image DEB-DQEAMPRN*.png
Saving 4093th image DEB-DMQNPCKH*.png
Saving 4094th image NORM-KKPYVFVT*.png
Saving 4095th image DEB-NKFGEAIR*.png
Saving 4096th image TUM-ENKVNKIP*.png
Saving 4097th image LYM-AYKWKLNG*.png
Saving 4098th image TUM-YDNFKPKY*.png
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Saving 4224th image ADI-FQVYIPKV*.png
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Saving 4226th image DEB-APEYRVFK*.png
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Saving 4228th image STR-AKMGCSVC*.png
Saving 4229th image ADI-FLEFNNAV*.png
Saving 4230th image MUC-YWQARCGM*.png
Saving 4231th image TUM-YERFHKYN*.png
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Saving 4233th image NORM-PFRLNCFQ*.png
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Saving 4318th image MUC-IVRCPYDQ*.png
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Saving 4353th image MUC-CGGKWASV*.png
Saving 4354th image MUC-IIQMKQID*.png
Saving 4355th image TUM-RRNYIYMG*.png
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Saving 4357th image LYM-TYILMCVF*.png
Saving 4358th image STR-KVKRGSDC*.png
Saving 4359th image TUM-HMWFDVMG*.png
Saving 4360th image MUC-NSKCNTFQ*.png
Saving 4361th image MUC-ICAHHGPC*.png
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Saving 4363th image TUM-LCSRLHGT*.png
Saving 4364th image MUC-LKFYGCTI*.png
Saving 4365th image BACK-EIWFIYIH*.png
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Saving 4367th image LYM-EEIKKVEL*.png
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Saving 4370th image LYM-AVGDEQTN*.png
Saving 4371th image NORM-IIMLEFIN*.png
Saving 4372th image DEB-TKVNLHAR*.png
Saving 4373th image ADI-RSPMPCSV*.png
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Saving 4378th image DEB-AWKDLKWE*.png
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Saving 4382th image STR-DRKMPYQY*.png
Saving 4383th image TUM-ELRDVFQI*.png
Saving 4384th image NORM-DMVATGKN*.png
Saving 4385th image MUC-MPRQEEMA*.png
Saving 4386th image TUM-RIESAVHQ*.png
Saving 4387th image ADI-DCGEYILL*.png
Saving 4388th image MUC-QMREPFCV*.png
Saving 4389th image TUM-ICYPFGYP*.png
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Saving 4398th image NORM-YETDKRSP*.png
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Saving 4400th image ADI-KEEIAHAE*.png
Saving 4401th image NORM-DIAMIEIV*.png
Saving 4402th image LYM-RLFLINHP*.png
Saving 4403th image LYM-YVQHLHWP*.png
Saving 4404th image MUC-ISHELRAT*.png
Saving 4405th image ADI-FAWIDFMW*.png
Saving 4406th image DEB-KKMWIGNI*.png
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Saving 4417th image ADI-MYGYDPVQ*.png
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Saving 4425th image MUS-SECRKVYW*.png
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Saving 4427th image LYM-IHMTAWSP*.png
Saving 4428th image ADI-CKAPVYTR*.png
Saving 4429th image BACK-PCTDWGTC*.png
Saving 4430th image MUC-WKLVNFGY*.png
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Saving 4433th image STR-RFDVSLTS*.png
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Saving 4435th image TUM-IVCMTLYT*.png
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Saving 4437th image TUM-AGGGAKMS*.png
Saving 4438th image LYM-PRCKAITG*.png
Saving 4439th image LYM-EAACGNYN*.png
Saving 4440th image LYM-IRHIHMRC*.png
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Saving 4444th image TUM-EPMPSIGL*.png
Saving 4445th image DEB-MLLGFYNI*.png
Saving 4446th image ADI-KFTWAKHT*.png
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Saving 4448th image MUS-CLLTTLFI*.png
Saving 4449th image TUM-DMVQAWFG*.png
Saving 4450th image TUM-SIVHFFIE*.png
Saving 4451th image MUS-KSPREEQN*.png
Saving 4452th image TUM-FFYFDMES*.png
Saving 4453th image MUS-KYMNFNTR*.png
Saving 4454th image STR-RNCQNQKG*.png
Saving 4455th image MUS-ILWIVIHP*.png
Saving 4456th image MUC-LEISMPAI*.png
Saving 4457th image NORM-PSHTRRWW*.png
Saving 4458th image TUM-EEAAEMSL*.png
Saving 4459th image STR-TACWTQLH*.png
Saving 4460th image LYM-KDMHASGR*.png
Saving 4461th image STR-RFKRWFYI*.png
Saving 4462th image MUS-SSDEDCQF*.png
Saving 4463th image DEB-TPNIVVEA*.png
Saving 4464th image STR-PEPYEYMM*.png
Saving 4465th image TUM-REYYKYKS*.png
Saving 4466th image ADI-ESRISMAA*.png
Saving 4467th image TUM-LHWNWVYK*.png
Saving 4468th image TUM-SIWKWMFI*.png
Saving 4469th image TUM-YWIDVGKP*.png
Saving 4470th image MUC-KLASCIIY*.png
Saving 4471th image LYM-LKFWVEYH*.png
Saving 4472th image DEB-IKFHWHEI*.png
Saving 4473th image DEB-TRPMRHWV*.png
Saving 4474th image STR-SPQRPIRE*.png
Saving 4475th image STR-YICKQQDE*.png
Saving 4476th image MUC-YLHYYKEG*.png
Saving 4477th image DEB-YCCDAWSK*.png
Saving 4478th image MUS-THPKPNWY*.png
Saving 4479th image MUS-SLTDGTMD*.png
Saving 4480th image STR-AQAAWKFQ*.png
Saving 4481th image MUS-IIVERECK*.png
Saving 4482th image ADI-ANQNAYVH*.png
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Saving 4484th image MUC-PMRLTPEE*.png
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Saving 4514th image TUM-EMCLCEYN*.png
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Saving 4516th image MUS-PYLHKNGL*.png
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Saving 4523th image DEB-RPNDTSMG*.png
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Saving 4537th image MUC-IARCHSND*.png
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Saving 4539th image ADI-FRQTGSWA*.png
Saving 4540th image STR-SLHRRPKP*.png
Saving 4541th image LYM-FFVQWDED*.png
Saving 4542th image TUM-VIMCLRIF*.png
Saving 4543th image MUC-YICSCRYF*.png
Saving 4544th image STR-CQTWAGLP*.png
Saving 4545th image LYM-VMNLFEHQ*.png
Saving 4546th image TUM-GQEVMIIY*.png
Saving 4547th image BACK-MRHYIQVN*.png
Saving 4548th image NORM-IYSFITQI*.png
Saving 4549th image STR-INKDFIIW*.png
Saving 4550th image BACK-NLETKVNP*.png
Saving 4551th image DEB-KLEFRCSQ*.png
Saving 4552th image MUC-IDASKNSK*.png
Saving 4553th image STR-PTWMDVTN*.png
Saving 4554th image STR-HRKPYAVL*.png
Saving 4555th image TUM-CEIEIGRP*.png
Saving 4556th image LYM-IVGGANDN*.png
Saving 4557th image DEB-LNSKWNWM*.png
Saving 4558th image ADI-ITRQPSNI*.png
Saving 4559th image TUM-RQRFDRNQ*.png
Saving 4560th image MUS-DKLPRPFA*.png
Saving 4561th image ADI-FYFFPMDP*.png
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Saving 4563th image STR-NTYRGFHF*.png
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Saving 4565th image NORM-QCWMDAVF*.png
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Saving 4567th image LYM-SLEHCGVL*.png
Saving 4568th image MUC-VFAKIRKN*.png
Saving 4569th image ADI-CHNQIMSD*.png
Saving 4570th image NORM-HWEMGYGR*.png
Saving 4571th image STR-NYNTKPFD*.png
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Saving 4653th image MUC-LDKVVFPG*.png
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Saving 4656th image NORM-LERCFRYW*.png
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Saving 4680th image MUC-TNYAQRVK*.png
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Saving 4689th image BACK-KLKEMCRT*.png
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Saving 4714th image BACK-EHVIWMCF*.png
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Saving 4716th image MUC-YCIFPVHR*.png
Saving 4717th image BACK-NPTSSMRM*.png
Saving 4718th image BACK-APILTDCC*.png
Saving 4719th image TUM-SCHAYCFV*.png
Saving 4720th image LYM-SERLDGID*.png
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Saving 4725th image BACK-NSVSHMNH*.png
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Saving 4760th image ADI-RHLWGTQR*.png
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Saving 4783th image TUM-IKIDCVAH*.png
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Saving 4800th image ADI-EFQFGGCP*.png
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Saving 4803th image LYM-QPTFCMWQ*.png
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Saving 4805th image NORM-QACHWRAH*.png
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Saving 4810th image ADI-PNWRKAPE*.png
Saving 4811th image BACK-KVQTNLID*.png
Saving 4812th image DEB-LTRLWPSG*.png
Saving 4813th image MUC-EKWVVFKE*.png
Saving 4814th image MUC-PFGWEHNQ*.png
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Saving 4816th image TUM-TKWQIARR*.png
Saving 4817th image TUM-WVNYTVQF*.png
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Saving 4819th image LYM-YVWQSQPP*.png
Saving 4820th image TUM-CGMMYSEP*.png
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Saving 4822th image LYM-QTKEPFPA*.png
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Saving 4824th image BACK-IILEDTGT*.png
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Saving 4829th image LYM-TFSRKCLV*.png
Saving 4830th image NORM-FCAAFADQ*.png
Saving 4831th image ADI-DMMMVGQI*.png
Saving 4832th image MUC-MTTFPVFR*.png
Saving 4833th image MUC-FAAAQGRT*.png
Saving 4834th image LYM-QYTCGMCG*.png
Saving 4835th image STR-TLQCEVCK*.png
Saving 4836th image STR-YWFVRSGP*.png
Saving 4837th image LYM-HCMGNMLV*.png
Saving 4838th image TUM-SMIGKLQF*.png
Saving 4839th image TUM-KVTCFGIN*.png
Saving 4840th image STR-ECQENRQD*.png
Saving 4841th image TUM-DIAAGPFD*.png
Saving 4842th image MUC-TIYPMMPI*.png
Saving 4843th image MUC-RPGQVIPV*.png
Saving 4844th image LYM-ARPPPTWQ*.png
Saving 4845th image TUM-EEHTYYHM*.png
Saving 4846th image STR-NLEVFIVS*.png
Saving 4847th image DEB-QFVPKSVR*.png
Saving 4848th image LYM-QSGNSFNG*.png
Saving 4849th image TUM-TDNCCIYM*.png
Saving 4850th image STR-WDMYCAKE*.png
Saving 4851th image NORM-APKSRMRR*.png
Saving 4852th image ADI-MEWGNGHP*.png
Saving 4853th image MUS-REKCWIAS*.png
Saving 4854th image NORM-FGAYQRES*.png
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Saving 4856th image LYM-GIIYQFNH*.png
Saving 4857th image MUS-TYDLRIEY*.png
Saving 4858th image MUC-TGRMLTAN*.png
Saving 4859th image STR-QTILSGQY*.png
Saving 4860th image TUM-RDYPYWHQ*.png
Saving 4861th image MUS-NSHSQVLD*.png
Saving 4862th image TUM-CWRWNEDP*.png
Saving 4863th image TUM-TRPMPPGC*.png
Saving 4864th image DEB-KWRMTATL*.png
Saving 4865th image BACK-ERNWNMHD*.png
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Saving 4867th image NORM-GQYPYRMC*.png
Saving 4868th image NORM-PPHKPNLF*.png
Saving 4869th image NORM-SITSPFNP*.png
Saving 4870th image LYM-RHNVCTQH*.png
Saving 4871th image LYM-CQGWMFSN*.png
Saving 4872th image MUS-SFGCDNWQ*.png
Saving 4873th image MUC-SAEVNGAR*.png
Saving 4874th image STR-GFRPVSYN*.png
Saving 4875th image TUM-VCKCIMVA*.png
Saving 4876th image TUM-DKHRPNYE*.png
Saving 4877th image DEB-AVCMSAQH*.png
Saving 4878th image LYM-QQMTKSCN*.png
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Saving 4880th image BACK-EQFQKSGW*.png
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Saving 4882th image STR-DRGHDFHT*.png
Saving 4883th image MUC-TYQIKHEC*.png
Saving 4884th image NORM-KFRLGYSS*.png
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Saving 4886th image MUS-FQHGTFEE*.png
Saving 4887th image DEB-CIYKKNPN*.png
Saving 4888th image STR-VNFRLSFH*.png
Saving 4889th image BACK-GYQWLAKN*.png
Saving 4890th image MUC-GPWMTDAM*.png
Saving 4891th image STR-EFYSFACQ*.png
Saving 4892th image MUC-EFVSKEHH*.png
Saving 4893th image ADI-NQKHYVMT*.png
Saving 4894th image MUS-GEWRCHQN*.png
Saving 4895th image DEB-CTCPYYNW*.png
Saving 4896th image ADI-FMLFVSMW*.png
Saving 4897th image MUS-ECAQSSEI*.png
Saving 4898th image MUC-MWCTCMCF*.png
Saving 4899th image STR-RNKHDYWE*.png
Saving 4900th image MUC-RRSNDVCL*.png
Saving 4901th image LYM-PVLYMGWD*.png
Saving 4902th image BACK-NGWREVCS*.png
Saving 4903th image DEB-WKRQVCII*.png
Saving 4904th image BACK-CFGPILWV*.png
Saving 4905th image NORM-LTKMMELG*.png
Saving 4906th image LYM-ESTTNRCG*.png
Saving 4907th image TUM-FGMDYKRQ*.png
Saving 4908th image DEB-FLGTTECT*.png
Saving 4909th image LYM-RMGGWCKC*.png
Saving 4910th image STR-IYWWQEKQ*.png
Saving 4911th image STR-QLPNGQHD*.png
Saving 4912th image MUC-TDVREKFY*.png
Saving 4913th image MUS-QAKGEYHC*.png
Saving 4914th image MUS-ITPKWDRF*.png
Saving 4915th image NORM-GFMWHPKY*.png
Saving 4916th image MUC-TVSKDRCT*.png
Saving 4917th image LYM-NNTYIRPN*.png
Saving 4918th image STR-KPVPLLSQ*.png
Saving 4919th image STR-TYWCDVMG*.png
Saving 4920th image STR-MWMQQLGF*.png
Saving 4921th image STR-PLNDILKQ*.png
Saving 4922th image NORM-ILQDTFQG*.png
Saving 4923th image MUC-WFEQNDKD*.png
Saving 4924th image STR-AHPCNLNF*.png
Saving 4925th image DEB-QRFYCLKI*.png
Saving 4926th image DEB-VIATKSWR*.png
Saving 4927th image BACK-CSYPNLFV*.png
Saving 4928th image LYM-QYMLRRTA*.png
Saving 4929th image BACK-LKTFSEKH*.png
Saving 4930th image ADI-SGFQKGPE*.png
Saving 4931th image MUC-PARMMRCF*.png
Saving 4932th image LYM-HIQAHLPF*.png
Saving 4933th image TUM-LMQGQFHF*.png
Saving 4934th image MUC-HDIVTPNQ*.png
Saving 4935th image BACK-KGEAEIID*.png
Saving 4936th image MUS-CLNAVINW*.png
Saving 4937th image STR-AAPNYWIR*.png
Saving 4938th image TUM-PRYMERQY*.png
Saving 4939th image ADI-NGMYSIPG*.png
Saving 4940th image LYM-LCSRCIPN*.png
Saving 4941th image TUM-RNDYWEIC*.png
Saving 4942th image NORM-GHVNPTHV*.png
Saving 4943th image LYM-QIVYLISK*.png
Saving 4944th image TUM-WYEAKMFF*.png
Saving 4945th image STR-KMNPPGEQ*.png
Saving 4946th image TUM-FIFLKLWK*.png
Saving 4947th image ADI-CYAKQTVG*.png
Saving 4948th image MUC-GVFWTENY*.png
Saving 4949th image LYM-HSNSSYED*.png
Saving 4950th image LYM-GVYWMSMV*.png
Saving 4951th image DEB-SKINSGPM*.png
Saving 4952th image LYM-TSGYEESP*.png
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Saving 5011th image MUC-RSQVSVNH*.png
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Saving 5013th image ADI-EQSQFGHE*.png
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Saving 5018th image TUM-MKAKFGPC*.png
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Saving 5022th image TUM-PNCMQNTC*.png
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Saving 5048th image TUM-WPCHPICD*.png
Saving 5049th image LYM-SFMSSKYC*.png
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Saving 5055th image LYM-YALYITSN*.png
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Saving 5059th image MUS-SGNHQMRV*.png
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Saving 5148th image DEB-PCYLWKGH*.png
Saving 5149th image TUM-HKCGGDMW*.png
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/usr/local/lib/python3.7/site-packages/skimage/color/colorconv.py:1128: UserWarning: Color data out of range: Z < 0 in 68 pixels
  return xyz2rgb(lab2xyz(lab, illuminant, observer))

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/usr/local/lib/python3.7/site-packages/skimage/color/colorconv.py:1128: UserWarning: Color data out of range: Z < 0 in 44 pixels
  return xyz2rgb(lab2xyz(lab, illuminant, observer))

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# Select random 4 results to display.
# Order: Input, Predicted, Ground Truth
# Assumes all three results folder have the 4 files

filelist = shuffle(os.listdir('results/predicted/'))
filelist = filelist[:4]

fig, ax = plt.subplots(4, 3, figsize=(16,16))
row = 0
for filename in filelist:
    folder = 'results/bnw/'
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255
    ax[row,0].imshow(image_in)
    ax[row,0].set_ylabel(filename)
    
    folder = 'results/predicted/'
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255
    ax[row,1].imshow(image_in)
    
    folder = 'results/actual/'
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255
    ax[row,2].imshow(image_in)
    
    row += 1

filelist = shuffle(os.listdir("results/predicted"))
filelist = filelist[:4]

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(16,16))
row=0

for filename in filelist:
    folder = "results/bnw/"
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255.
    axes[row,0].imshow(image_in)
    axes[row,0].set_ylabel(filename)
    
    folder = "results/predicted/"
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255.
    axes[row,1].imshow(image_in)
    
    folder = "results/actual/"
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255.
    axes[row,2].imshow(image_in)

    row+=1

filelist = shuffle(os.listdir("results/predicted"))
filelist = filelist[:4]

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(16,16))
row=0

for filename in filelist:
    folder = "results/bnw/"
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255.
    axes[row,0].imshow(image_in)
    axes[row,0].set_ylabel(filename)
    
    folder = "results/predicted/"
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255.
    axes[row,1].imshow(image_in)
    
    folder = "results/actual/"
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255.
    axes[row,2].imshow(image_in)

    row+=1

filelist = shuffle(os.listdir("results/predicted"))
filelist = filelist[:4]

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(16,16))
row=0

for filename in filelist:
    folder = "results/bnw/"
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255.
    axes[row,0].imshow(image_in)
    axes[row,0].set_ylabel(filename)
    
    folder = "results/predicted/"
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255.
    axes[row,1].imshow(image_in)
    
    folder = "results/actual/"
    image_in = img_to_array(load_img(folder + filename))
    image_in = image_in/255.
    axes[row,2].imshow(image_in)

    row+=1